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The Comparison of Password Composition Policies Among US, German, and Thailand Samples 20

The Comparison of Password Composition Policies Among US, Ge...

引用

20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Lomchan, Jenjira Wiangsripanawan, Rungrat Shahandashti, Siamak F. School of Science King Mongkut's Institute of Technology Ladkrabang Dept. of Computer Science Bangkok Thailand University of York Dept. of Computer Science York United Kingdom

ISBN: (纸本)9798350300505

The study by Mayer, Kirchner, and Volkamer published at SOUPS 2017 showed that the password composition policy (PCP) strength of both the US and German websites was not influenced by the security but by the usability features of the websites. Surprisingly, the PCP strength of the banking website category was the lowest, whereas the government website was the highest. Our aim in conducting the first study is to find whether 78 Thai frequently used websites in 2018 would yield the same surprising results. Our finding showed an opposite perspective, the highest PCP strength was from the banking websites, followed by university and government websites, respectively. Two more security features were added to our study: 2FA and HTTPS. Although some German websites employing 2FA allowed lower PCPs for better usability, Thai websites with 2FA did not loosen the password requirements. Also, employing HTTPS did not impact the PCP strength. The study with Thai websites was reinvestigated in 2021, two years after the Personal data Protection Act (PDPA) was announced. The result showed that the median PCP strength of all Thailand samples had grown from 26.6 in 2018 to 31.0 in 2021. The banking websites still retained the highest PCP strength. A significant change appeared on the government websites, increasing from 29.9 to 40.4. In summary, the security features such as the size of services, and values of assets which play no part in both the US and German PCPs were heavily concerned by Thai websites. Government and university websites in Germany and USA gave much higher PCP strength than those in Thailand. The Thai government's PCP strength sharply increased in 2021 due to the privacy law. Nevertheless, it was still lower than the results in Germany and USA in 2016. Therefore, the criteria influencing PCP vary depending on the country. © 2023 IEEE.

关键词： Websites

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Musical Brainwaves: Decoding Electroencephalography Signals Elicited by Musical Stimuli 1

Musical Brainwaves: Decoding Electroencephalography Signals ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Ankitha C Juneja, Hita Bharadwaj, Ishita Varghese, Jeffrey S Honnavalli, Prasad B. Srinivasa, Gowri PES University Dept. of Computer Science Engg. Bengaluru India PES University PES Center for Information Security Forensics and Cyber Resilience Dept. of Computer Science Engg. Bengaluru India PES University PES Center of Pattern Recognition Dept. of Computer Science Engg. Bengaluru India

ISBN: (纸本)9798350353648

The influence of music on the human brain has been recognized for centuries, but recent advancements in brain-computer interfaces (BCIs) offer unparalleled opportunities to probe its complexities. This study investigates the effectiveness of music as a stimulus for driving BCIs using electroencephalography (EEG) signals. We analyze the neural correlates of music perception and imagination in the OpenMIIR dataset, employing advanced machine learning algorithms for EEG decoding. Exploiting various feature engineering techniques in time domain and frequency domain, we leverage convolutional neural networks and speech-inspired architectures like VLAAI to reconstruct the original audio stimuli from EEG data. Our results demonstrate a significant improvement in reconstructing music stimuli from EEG signals in the time domain, achieving a Pearson correlation of 0.35 compared to the current state-of-the-art performance of 0.11. Similarly, an increase in accuracy in classifying lyrical and non-lyrical musical excerpts based on participants' EEG responses was observed, rising from 49.6% to 57%. © 2024 IEEE.

关键词： Frequency domain analysis

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Bibliometric Analysis of IoT Application Research: Unveiling Trends and Future Directions for Terahertz Communication in Wireless Systems 4

Bibliometric Analysis of IoT Application Research: Unveiling...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Hajoary, Deepak Basumatary, Rinku Narzary, Raju Dept. of Management Studies Assam Kokrajhar India Dept. of Computer Science and Technology Assam Kokrajhar India

ISBN: (纸本)9798350387490

This abstract presents a bibliometric analysis of IoT application research, focusing on unveiling trends and future directions in the field of terahertz communication for wireless systems. The study employs bibliometric techniques to analyze and assess the existing literature on IoT applications, with a specific emphasis on terahertz communication. By examining the publication trends, citation patterns, and collaboration networks, this research provides valuable insights into the current state of terahertz communication in the context of IoT. The findings highlight the emerging applications, key research areas, and potential challenges in terahertz communication for wireless systems. This bibliometric analysis serves as a roadmap for researchers and industry professionals interested in further advancing the field, shaping the future of IoT applications with terahertz communication technologles. © 2024 IEEE.

关键词： 5G mobile communication systems

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Recognition of Fingerprint Images using CNN for Cybercrime Detection System 2

Recognition of Fingerprint Images using CNN for Cybercrime D...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kavitha, T. Patil, Bhimaraya Saraswathi, S. Rajarajeswari, S. Patil, Anita New Horizon College of engineering Dept. of Computer Engineering Bangalore India Sri Sivasubramaniya Nadar College of Engineering Dept. of Computer Science Engineering Chennai India Vellore Institute of Technology School of computer science and engineering Vellore India City Engineering College Dept. of Information Science Bengaluru India

ISBN: (纸本)9798350372892

Fingerprint identification plays a crucial role in crime detection, but manual analysis is time-consuming and error-prone. This research aims to develop an automated system for accurate and efficient fingerprint matching using machine learning. The proposed approach involves image acquisition, preprocessing, feature extraction, and classification. Fingerprint images are enhanced, binarized, and segmented. Minutiae (ridge endings and bifurcations) are extracted as features. A convolutional neural network (CNN) is trained on a dataset of fingerprint images for classification. By comparing algorithms, Random Forest achieved over 8 1 % accuracy, outperforming Decision Trees (nearly 75% accuracy). The trained CNN model can accurately match fingerprints from crime scenes with potential suspects, providing reliable evidence. The developed system offers law enforcement agencies an automated and accurate fingerprint identification tool, improving crime-solving efficiency and reducing manual effort. The proposed approach showcases the potential of machine learning in forensic applications. © 2024 IEEE.

关键词： Random forests

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Exploring the Impact of Conditional Deep Convolutional Generative Adversarial Networks in Brain Tumor Image Classification: A Novel Approach

Exploring the Impact of Conditional Deep Convolutional Gener...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kuj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331508685

In this paper, we explore the impact of conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) with brain tumor image classification and introduce a new way to improve diagnosis accuracy. Shortage of data/data Imbalance: In Medical Imaging datasets, it helps in surging the data scarcity and class imbalance with mimicking generation images using cDCGAN. The detailed investigation demonstrates that synthetic data effectively improves classifier performance by acting as a form of augmentation, providing additional training examples for classifiers and serving as ground truth. From the computational efficiency analysis, we see that there is also scalability in training time with respect to dataset size. Consequently, this study presents cDCGANs as an important asset with which to improve diagnostic accuracy through brain tumor categorization in clinical contexts. © 2025 IEEE.

关键词： Generative adversarial networks

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EEG-driven GAN for Alpha Rhythm Generation in Passive BCI 3

EEG-driven GAN for Alpha Rhythm Generation in Passive BCI

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Cinquetti, Ettore Zanni, Guglielmo Menegaz, Gloria Storti, Silvia F. University of Verona Dept. of Computer Science Italy University of Verona Dept. of Engineering for Innovation Medicine Italy

ISBN: (纸本)9798350378009

Passive brain-computer interface (BCI) based on electroencephalographic (EEG) signals show promise for monitoring human vigilance in critical settings that continuously strain human attention. However, the lack of extensive public datasets limits progress in artificial intelligence research. To overcome this problem, we implemented generative adversarial networks (GANs) to augment existing EEG datasets. We focused on two datasets: a publicly available resting-state dataset and a custom one simulating industrial activities. After extracting time-variant alpha indices using continuous wavelet transform, we compared the generated data with real data using the L2 distance metric and autocorrelation function. We additionally assessed two forecasting models trained on original and augmented datasets, comparing their predictions. The integration of synthetic data led to an improvement in signal behavior prediction, as evidenced by a 32% reduction in mean absolute error for the resting-state dataset. Furthermore, a metric inspired by the Frechét Inception Distance was computed using the forecasting model to discern the distributions of embeddings for the real and generated data, with results showing a strong resemblance between the two. This study challenges the limitation of acquiring domain-specific EEG data and aims to develop a robust signal generation framework. © 2024 IEEE.

关键词： Wavelet transforms

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Automating Dermatological Diagnosis - A Comparative Analysis 5

Automating Dermatological Diagnosis - A Comparative Analysis

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Johnson, Aleen Arora, Vishnupriya Aggarwal, Lakshita Goswami, Puneet Srm University Dept. Of Computer Science & Engineering Haryana Sonepat India Srm University Dept. Of Computer Science Haryana Sonepat India

ISBN: (纸本)9798350330861

Evolving and adapting healthcare along with growing technology is an urgent need of an hour using machine learning is an addition to classify health data for the large aspect of identifying dermatological illness. Convolution Neural Network (CNN) can accurately classify a variety of skin illnesses by extracting complex information from images. CNN do away with the requirement for manual feature engineering because they are able to learn from large amounts of data. They are resistant to noise and changes in lighting, ensuring accurate analysis every time. CNN help in early detection, increasing the efficacy of treatment, and lowering the possibility of a false positive. Their combination has the potential to greatly improve patient care and change dermatological procedures. Objective: The goal of this study is to determine the best effective algorithm, whether it comes from deep learning or machine learning, based on a thorough comparative analysis that considers elements like accuracy, interpret ability, and scalability. Methodology: This study uses a comparative analysis methodology to decide which machine learning and deep learning algorithms are the most effective. It entails a review of the literature, the gathering of data, the design and execution of experiments, statistical analysis, and result interpretation. Findings: This study's findings shows that deep learning approaches are more effective than machine learning ones. In contrast to machine learning, which depends on manual, subjective techniques, deep learning allows for automatic and objective feature extraction. Deep learning achieves greater prediction accuracy and generalization ability despite requiring more training time and computational resources, demonstrating its superior learning powers. © 2023 IEEE.

关键词： Learning algorithms

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Uncovering Linguistic Patterns: Exploring Ensemble Learning and Low-Level Features for Identifying Spoken Arabic, English, Spanish, and German 5

Uncovering Linguistic Patterns: Exploring Ensemble Learning ...

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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Hamdi, Skander Moussaoui, Abdelouahab Chabane, Mafaza Laouarem, Ayoub Berrimi, Mohamed Oussalah, Mourad University of Ferhat Abbes Setif 1 Dept. of Computer Science Setif Algeria University of Oulu Dept. of Computer Science Oulu Finland

ISBN: (纸本)9798350381450

This paper presents a novel approach to spoken language identification in Arabic, English, Spanish, and German languages using ensemble learning techniques. The study compares the performance of two well-known ensemble learning algorithms (Random Forest and XGBoost). Next, a Stacking Ensemble method with Logistic Regression is used as a meta-model, which combines the predictions of two configurations of Random Forest and two other configurations of XGBoost. We explore the utilization of the audio Low-Level Descriptors features, which have previously received limited attention in spoken language identification. Experimental results demonstrate the effectiveness of the ensemble learning algorithms, achieving high accuracy in accurately classifying spoken languages. Notably, the Stacking Ensemble method showcases its ability to reduce misclassification rate, emphasizing its potential for performance improvement. The stacking Ensemble method achieved the highest recorded classification rate of 97.22%, outperforming individual methods. © 2023 IEEE.

关键词： Learning systems

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Voice AI-Intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's Machine Learning Kit Machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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